{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import numpy as np\n",
    "import sys\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取和读取数据\n",
    "def load_fashion_mnist(batch_size):\n",
    "    mnist_train = torchvision.datasets.FashionMNIST(root=\"Datasets/FashionMNIST\", train=True, download=True, transform=transforms.ToTensor())\n",
    "    mnist_test = torchvision.datasets.FashionMNIST(root=\"Datasets/FashionMNIST\", train=False, download=True, transform=transforms.ToTensor())\n",
    "    if sys.platform.startswith('win'):\n",
    "        num_workers = 0 # 0表示不用额外的进程来加速读取数据\n",
    "    else:\n",
    "        num_workers = 4\n",
    "    train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "    test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "    return train_iter, test_iter\n",
    "\n",
    "batch_size = 256\n",
    "train_iter, test_iter = load_fashion_mnist(batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化模型参数\n",
    "# 已知每个样本输入是高和宽均为28像素的图像。\n",
    "# 模型的输入向量的长度是 28×28=78428×28=784\n",
    "# 由于图像有10个类别，单层神经网络输出层的输出个数为10\n",
    "# softmax回归的权重和偏差参数分别为784×10和1×10的矩阵\n",
    "\n",
    "num_inputs = 28 * 28\n",
    "num_outputs = 10\n",
    "w = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)), dtype=torch.float)\n",
    "b = torch.zeros(num_outputs, dtype=torch.float)\n",
    "w.requires_grad_(requires_grad=True)\n",
    "b.requires_grad_(requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[5, 7, 9]])\n",
      "tensor([[ 6],\n",
      "        [15]])\n"
     ]
    }
   ],
   "source": [
    "# 实现Softmax运算\n",
    "\n",
    "X = torch.tensor([[1, 2, 3], [4, 5, 6]])\n",
    "print(X.sum(dim=0, keepdim=True)) # 对同一列的元素求和\n",
    "print(X.sum(dim=1, keepdim=True)) # 对同一行的元素求和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# softmax运算会先通过exp函数对每个元素做指数运算，\n",
    "# 再对exp矩阵同行元素求和，最后令矩阵每行各元素与该行元素之和相除\n",
    "# softmax运算的输出矩阵中的任意一行元素代表了一个样本在各个输出类别上的预测概率\n",
    "\n",
    "def softmax(X):\n",
    "    # 先对每个元素做指数运算\n",
    "    X_exp = X.exp()\n",
    "    # 每一行分别求和，并保持维度不变\n",
    "    partition = X_exp.sum(dim=1, keepdim=True)\n",
    "    return X_exp / partition # 这里用到了广播机制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.6356, 0.3064, 0.4678, 0.6073, 0.1801],\n",
      "        [0.8001, 0.8828, 0.2596, 0.1954, 0.6229]])\n",
      "tensor([[0.2397, 0.1725, 0.2027, 0.2331, 0.1520],\n",
      "        [0.2468, 0.2680, 0.1437, 0.1348, 0.2067]])\n",
      "tensor([[1.0000],\n",
      "        [1.0000]])\n"
     ]
    }
   ],
   "source": [
    "X = torch.rand((2, 5))\n",
    "X_prob = softmax(X)\n",
    "print(X)\n",
    "print(X_prob)\n",
    "print(X_prob.sum(dim=1, keepdim=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义模型\n",
    "\n",
    "def net(X):\n",
    "    return softmax(torch.mm(X.view((-1, num_inputs)), w) + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.1000],\n",
       "        [0.5000]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义损失函数\n",
    "y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
    "y = torch.LongTensor([0, 2])\n",
    "y_hat.gather(dim=1, index=y.view(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cross_entropy(y_hat, y):\n",
    "    return - torch.log(y_hat.gather(dim=1, index=y.view(-1, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算分类准确率\n",
    "# 给定一个类别的预测概率分布y_hat，将预测概率最大的类别作为输出类别\n",
    "# 分类准确率即正确预测数量与总预测数量之比\n",
    "\n",
    "def accuracy(y_hat, y):\n",
    "    return (y_hat.argmax(dim=1) == y).float().mean().item()\n",
    "\n",
    "# y_hat.argmax(dim=1)返回矩阵y_hat每行中最大元素的索引，\n",
    "# 且返回结果与变量y形状相同\n",
    "# (y_hat.argmax(dim=1) == y)是一个类型为ByteTensor的Tensor，\n",
    "# 使用float()将其转换为值为0（相等为假）或1（相等为真）的浮点型Tensor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    }
   ],
   "source": [
    "# 预测\n",
    "y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
    "# 真实\n",
    "y = torch.LongTensor([0, 2])\n",
    "\n",
    "print(accuracy(y_hat, y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 评价模型net在数据集data_iter上的准确率\n",
    "def evaluate_accuracy(data_iter, net):\n",
    "    acc_sum, n = 0.0, 0\n",
    "    for X, y in data_iter:\n",
    "        acc_sum += (net(X).argmax(dim=1)==y).float().sum().item()\n",
    "        n += y.shape[0]\n",
    "    return acc_sum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1407\n"
     ]
    }
   ],
   "source": [
    "# 随机初始化模型net，随机模型的准确率应该接近于类别个数10的倒数即0.1\n",
    "\n",
    "print(evaluate_accuracy(test_iter, net))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练模型\n",
    "# 使用小批量随机梯度下降来优化模型的损失函数\n",
    "\n",
    "num_epochs = 5\n",
    "lr = 0.1\n",
    "\n",
    "def sgd(params, lr, batch_size):\n",
    "    for param in params:\n",
    "        # 注意这里更改param时用的param.data\n",
    "        param.data -= lr * param.grad / batch_size\n",
    "\n",
    "def train_model(net, train_iter, test_iter, loss, num_epochs, batch_size,\n",
    "             params=None, lr=None, optimizer=None):\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum, train_acc_sum, n = 0.0, 0.0, 0\n",
    "        for X, y in train_iter:\n",
    "            # 计算出的概率分布\n",
    "            y_hat = net(X)\n",
    "            l = loss(y_hat, y).sum() # 注意这里不是mean(),所以在sgd()中除以了batch_size\n",
    "            \n",
    "            # 梯度清零\n",
    "            if optimizer is not None:\n",
    "                optimizer.zero_grad()\n",
    "            elif params is not None and params[0].grad is not None:\n",
    "                for param in params:\n",
    "                    param.grad.data.zero_()\n",
    "            \n",
    "            # 反向传播更新参数\n",
    "            l.backward()\n",
    "            if optimizer is None:\n",
    "                sgd(params, lr, batch_size)\n",
    "            else:\n",
    "                optimizer.step()\n",
    "            \n",
    "            train_l_sum += l.item()\n",
    "            train_acc_sum += (y_hat.argmax(dim=1)==y).sum().item()\n",
    "            n += y.shape[0]\n",
    "        # 训练过程中，在测试集上的准确率\n",
    "        test_acc = evaluate_accuracy(test_iter, net)\n",
    "        \n",
    "        print('epoch %d, loss %.4f, train_acc %.3f, test_acc %.3f'\n",
    "             %(epoch+1, train_l_sum/n, train_acc_sum/n, test_acc))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 0.7847, train_acc 0.751, test_acc 0.791\n",
      "epoch 2, loss 0.5708, train_acc 0.813, test_acc 0.813\n",
      "epoch 3, loss 0.5259, train_acc 0.825, test_acc 0.820\n",
      "epoch 4, loss 0.5011, train_acc 0.833, test_acc 0.820\n",
      "epoch 5, loss 0.4866, train_acc 0.836, test_acc 0.827\n"
     ]
    }
   ],
   "source": [
    "train_model(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size, [w,b], lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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   "source": [
    "# 预测\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def use_svg_display():\n",
    "    # 用矢量图显示\n",
    "    display.set_matplotlib_formats('svg')\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    # 设置图的尺寸\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "    \n",
    "def show_fashion_mnist(images, labels):\n",
    "    use_svg_display()\n",
    "    _, figs = plt.subplots(1, len(images), figsize=(10,10))\n",
    "    for f, img, label in zip(figs, images, labels):\n",
    "        f.imshow(img.view(28,28).numpy())\n",
    "        f.set_title(label, fontsize=8, color='b')\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()\n",
    "\n",
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [text_labels[int(each)] for each in labels]\n",
    "\n",
    "X, y = iter(test_iter).next()\n",
    "true_labels = get_fashion_mnist_labels(y.numpy())\n",
    "pred_labels = get_fashion_mnist_labels(net(X).argmax(dim=1).numpy())\n",
    "titles = [true+'\\n'+pred for true,pred in zip(true_labels,pred_labels)]\n",
    "show_fashion_mnist(X[0:10], titles[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1, loss 0.7848, train_acc 0.749, test_acc 0.778\n",
      "epoch 2, loss 0.5717, train_acc 0.812, test_acc 0.808\n",
      "epoch 3, loss 0.5246, train_acc 0.826, test_acc 0.818\n",
      "epoch 4, loss 0.5010, train_acc 0.832, test_acc 0.824\n",
      "epoch 5, loss 0.4853, train_acc 0.837, test_acc 0.826\n"
     ]
    }
   ],
   "source": [
    "# 整个模型\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "# 图像数据展示相关\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "def use_svg_display():\n",
    "    display.set_matplotlib_formats('svg') # 用矢量图显示\n",
    "\n",
    "def set_figsize(figsize=(3.5, 2.5)):\n",
    "    use_svg_display()\n",
    "    plt.rcParams['figure.figsize'] = figsize # 设置图的尺寸\n",
    "    \n",
    "def show_fashion_mnist(images, labels):\n",
    "    use_svg_display()\n",
    "    _, figs = plt.subplots(1, len(images), figsize=(10,10))\n",
    "    for f, img, label in zip(figs, images, labels):\n",
    "        f.imshow(img.view(28,28).numpy())\n",
    "        f.set_title(label, fontsize=8, color='b')\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()\n",
    "\n",
    "def get_fashion_mnist_labels(labels):\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat','sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [text_labels[int(each)] for each in labels]\n",
    "\n",
    "# 获取和读取数据相关\n",
    "import sys\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "def load_fashion_mnist(batch_size):\n",
    "    mnist_train = torchvision.datasets.FashionMNIST(root=\"Datasets/FashionMNIST\", train=True, download=True, transform=transforms.ToTensor())\n",
    "    mnist_test = torchvision.datasets.FashionMNIST(root=\"Datasets/FashionMNIST\", train=False, download=True, transform=transforms.ToTensor())\n",
    "    if sys.platform.startswith('win'):\n",
    "        num_workers = 0 # 0表示不用额外的进程来加速读取数据\n",
    "    else:\n",
    "        num_workers = 4\n",
    "    train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)\n",
    "    test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)\n",
    "    return train_iter, test_iter\n",
    "\n",
    "# softmax运算\n",
    "def softmax(X):\n",
    "    X_exp = X.exp() # 先对每个元素做指数运算\n",
    "    partition = X_exp.sum(dim=1, keepdim=True) # 每一行分别求和，并保持维度不变\n",
    "    return X_exp / partition\n",
    "\n",
    "# 定义模型\n",
    "def net(X):\n",
    "    return softmax(torch.mm(X.view((-1, num_inputs)), w) + b)\n",
    "\n",
    "# 损失函数（交叉熵损失）\n",
    "def cross_entropy(y_pred, y):\n",
    "    return - torch.log(y_pred.gather(dim=1, index=y.view(-1, 1))) # 注意这里的各样本数据的损失没有叠加，是分开表示的\n",
    "\n",
    "# 分类准确度计算\n",
    "def accuracy(y_pred, y):\n",
    "    return (y_pred.argmax(dim=1) == y).float().mean().item()\n",
    "\n",
    "# 评价模型在整个数据集准确率（主要是测试数据集）\n",
    "def evaluate_accuracy(data_iter, net):\n",
    "    acc_sum, n = 0.0, 0\n",
    "    for X, y in data_iter:\n",
    "        acc_sum += (net(X).argmax(dim=1)==y).float().sum().item()\n",
    "        n += y.shape[0]\n",
    "    return acc_sum / n\n",
    "\n",
    "# 网络训练相关\n",
    "# 随机梯度下降更新参数\n",
    "def sgd(params, lr, batch_size):\n",
    "    for param in params:\n",
    "        param.data -= lr * param.grad / batch_size # 注意这里更改param时用的param.data\n",
    "\n",
    "# 模型训练的函数\n",
    "def train_model(net, train_iter, test_iter, loss, num_epochs, batch_size, params=None, lr=None, optimizer=None):\n",
    "    for epoch in range(num_epochs):\n",
    "        train_l_sum, train_acc_sum, n = 0.0, 0.0, 0\n",
    "        for X, y in train_iter:\n",
    "            y_pred = net(X) # 计算出的概率分布\n",
    "            l = loss(y_pred, y).sum() # 注意这里不是mean(),所以在sgd()中除以了batch_size\n",
    "            # 梯度清零\n",
    "            if optimizer is not None:\n",
    "                optimizer.zero_grad()\n",
    "            elif params is not None and params[0].grad is not None:\n",
    "                for param in params:\n",
    "                    param.grad.data.zero_()\n",
    "            # 反向传播\n",
    "            l.backward()\n",
    "            # 更新参数\n",
    "            if optimizer is None:\n",
    "                sgd(params, lr, batch_size)\n",
    "            else:\n",
    "                optimizer.step()\n",
    "            # 训练中的一些信息\n",
    "            train_l_sum += l.item()\n",
    "            train_acc_sum += (y_pred.argmax(dim=1)==y).sum().item()\n",
    "            n += y.shape[0]\n",
    "        # 训练过程中，在测试集上的准确率\n",
    "        test_acc = evaluate_accuracy(test_iter, net)\n",
    "        print('epoch %d, loss %.4f, train_acc %.3f, test_acc %.3f' % (epoch+1, train_l_sum/n, train_acc_sum/n, test_acc))\n",
    "        \n",
    "# 初始化模型及参数，开始训练\n",
    "num_inputs = 28 * 28 # 每个样本的特征数\n",
    "num_outputs = 10 # 输出一共有所少个类别\n",
    "w = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)), dtype=torch.float)\n",
    "b = torch.zeros(num_outputs, dtype=torch.float)\n",
    "w.requires_grad_(requires_grad=True)\n",
    "b.requires_grad_(requires_grad=True)\n",
    "\n",
    "batch_size = 256\n",
    "train_iter, test_iter = load_fashion_mnist(batch_size)\n",
    "num_epochs = 5\n",
    "lr = 0.1\n",
    "train_model(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size, [w,b], lr)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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